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India set two Nationally Determined Contribution targets to achieve the net zero carbon emission goal by 2070, which requires deep decarbonization of India's power generation sector. Yet, coal power generation contributes to more than 60\% of its total power generation, and policies still permit further coal fleet expansion and lifetime extensions. In this paper, we investigate the role of retrofitting India's coal plants for carbon capture and storage and biomass co-firing in developing the net-zero power system. We model the power generation and transmission network expansions across 30 Indian states in four representative grid evolution scenarios under progressively tighter carbon emission caps, taking into account sub-national coal price variation and thermal efficiency of individual coal plants. We find that coal plant retrofitting could happen by 2035 when an annual carbon cap for the power generation sector is less than 1,000 million tons CO$_2$. This reduces the unabated coal plant capacity, electricity generation, and carbon abatement costs. Exploiting renewable energy potentials solely, such as wind resources, could reduce carbon abatement costs significantly but will result in low coal plant utilization and uneven renewable generation deployment between Southern and Central regions concerning energy justice.

India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60\% of India's electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India's coal power generation as the country transitions towards its renewable energy targets.

Coal power plants will need to be phased out and face stranded asset risks under the net-zero energy system transition. Repurposing coal power plants could recoup profits and reduce carbon emissions using the existing infrastructure and grid connections. This paper investigates a retrofitting strategy that turns coal power plants into thermal energy storage (TES) and zero-carbon data centers (DCs). The proposed capacity expansion model considers the co-locations of DCs, local renewablewith the system-generation, andlevel coal retir energy storage ement and retrofitting. We optimize the DC system configurations under the hourly-matching carbon policy and flexible operations. Results show that under hourly-matching carbon constraints, the retrofitted TES could complement the operations of lithium-ion batteries (LIBs) to reduce system costs. This could render DCs with optimal co-located renewable generations and energy storage more cost-effective than unconstrained DCs.

Global energy sector decarbonization efforts are contingent on technology choices for energy production and end-use in emerging markets such as India, where air conditioning is expected to be a major driver for electricity demand growth. Here, we use an integrated demand-supply framework to quantify the impacts of demand growth and temporal patterns on long-term electricity system evolution. Under projected renewables and Li-ion storage cost declines, our supply-demand modeling points to renewables contributing substantially (46-67%) to meet annual electricity demand in India by 2030. However, without appropriate policy measures to phase out existing coal generation, even such rapid adoption of renewable energy coupled with one or more technological levers such as low-cost energy storage and demand-side measures such as setting aggressive AC efficiency standards and deploying distribution level storage, are insufficient to reduce annual CO2 emissions in 2050 vs. 2020 because of the relatively higher growth rate of projected electricity demand over this period.

The growing demand for electricity in emerging markets and developing economies (EMDE) such as India is causing loading and congestion problems on distribution networks, particularly in urban locations, that adversely impact sustainable development and economic growth. Electric utilities in these economies face unique constraints regarding raising capital required to upgrade their congested networks. Battery storage has emerged as a non-wire alternative (NWA) to feeder upgrades. This article presents a flexible valuation framework for battery storage use in distribution networks and its application in the context of EMDE distribution network planning. We evaluate the value of storage as an NWA using a multi-stage decision making process that combines system optimization with markov-decision processes (MDP) to identify the least-cost network upgrade strategy under demand growth uncertainty. When applied to feeders in Delhi, India, the approach highlights the cost-effectiveness of battery storage to manage load growth while deferring network investments. Across the low, medium and high battery storage capital cost projections for 2030, we estimate that 18 to 29 GWh of battery storage capacity could be deployed to defer 11,752 to 15,914 km of medium voltage distribution feeder lines that are loaded at 60\% or more of their ampere capacity in 2030, resulting in 12 to 16\% capital cost savings. Interestingly, the lowering storage capital costs does not always increase NWA storage deployment, due to network capacity constraints limiting opportunities for off-peak storage charging.

India is expected to witness rapid growth in electricity use over the next two decades. Here, we introduce a custom regression model to project electricity consumption in India over the coming decades, which includes a bottom-up estimate of electricity consumption for two major growth drivers, air conditioning, and vehicle electrification. The model projections are available at a customizable level of spatial aggregation at an hourly temporal resolution, which makes them useful as inputs to long-term electricity infrastructure planning studies. The approach is used to develop electricity consumption data sets spanning various technology adoption and growth scenarios up to the year 2050 in five-year increments. The aim of the data is to provide a range of scenarios for India's demand growth given new technology adoption. With long-term hourly demand projections serving as an essential input for electricity infrastructure modeling, this data publication enables further work on energy efficiency, generation, and transmission expansion planning for a fast-growing and increasingly important region from a global climate mitigation perspective.